R Tutorial

An introduction to R

Introduction

This tutorial is will introduce the reader to , a free, open-source statistical computing environment often used with RStudio, a integrated development environment for .

R Project Logo

Download

Download at https://www.r-project.org/

Download RStudio at https://rstudio.com/products/rstudio/download/

Calculator

can be used as a super awesome calculator

# 5 + 3 = 8
5 + 3

## [1] 8

# 24 / (1 + 2) = 8
24 / (1 + 2)

## [1] 8

# 2 * 2 * 2 = 8
2^3

## [1] 8

# 8 * 8 = 64
sqrt(64)

## [1] 8

# -log10(0.05 / 5000000) = 8
-log10(0.05 / 5000000)

## [1] 8

Functions

has many useful built in functions

1:10

##  [1]  1  2  3  4  5  6  7  8  9 10

as.character(1:10)

##  [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10"

rep(1:2, times = 5)

##  [1] 1 2 1 2 1 2 1 2 1 2

rep(1:5, times = 2)

##  [1] 1 2 3 4 5 1 2 3 4 5

rep(1:5, each = 2)

##  [1] 1 1 2 2 3 3 4 4 5 5

rep(1:5, length.out = 7)

## [1] 1 2 3 4 5 1 2

seq(5, 50, by = 5)

##  [1]  5 10 15 20 25 30 35 40 45 50

seq(5, 50, length.out = 5)

## [1]  5.00 16.25 27.50 38.75 50.00

paste(1:10, 20:30, sep = "-")

##  [1] "1-20"  "2-21"  "3-22"  "4-23"  "5-24"  "6-25"  "7-26"  "8-27"  "9-28"  "10-29" "1-30"

paste(1:10, collapse = "-")

## [1] "1-2-3-4-5-6-7-8-9-10"

paste0("x", 1:10)

##  [1] "x1"  "x2"  "x3"  "x4"  "x5"  "x6"  "x7"  "x8"  "x9"  "x10"

min(1:10)

## [1] 1

max(1:10)

## [1] 10

range(1:10)

## [1]  1 10

mean(1:10)

## [1] 5.5

sd(1:10)

## [1] 3.02765

Custom Functions

Users can also create their own functions

customFunction1 <- function(x, y) {
  z <- 100 * x / (x + y)
  paste(z, "%")
}
customFunction1(x = 10, y = 90)

## [1] "10 %"

customFunction2 <- function(x) {
  mymin <- mean(x - sd(x))
  mymax <- mean(x) + sd(x)
  print(paste("Min =", mymin))
  print(paste("Max =", mymax))
}
customFunction2(x = 1:10)

## [1] "Min = 2.47234964590251"
## [1] "Max = 8.52765035409749"

`for` loops and `if` `else` statements

xx <- NULL #creates and empty object
for(i in 1:10) {
  xx[i] <- i*3
}
xx

##  [1]  3  6  9 12 15 18 21 24 27 30

xx %% 2 #gives the remainder when divided by 2

##  [1] 1 0 1 0 1 0 1 0 1 0

for(i in 1:length(xx)) {
  if((xx[i] %% 2) == 0) {
    print(paste(xx[i],"is Even"))
  } else { 
      print(paste(xx[i],"is Odd")) 
    }
}

## [1] "3 is Odd"
## [1] "6 is Even"
## [1] "9 is Odd"
## [1] "12 is Even"
## [1] "15 is Odd"
## [1] "18 is Even"
## [1] "21 is Odd"
## [1] "24 is Even"
## [1] "27 is Odd"
## [1] "30 is Even"

# or
ifelse(xx %% 2 == 0, "Even", "Odd")

##  [1] "Odd"  "Even" "Odd"  "Even" "Odd"  "Even" "Odd"  "Even" "Odd"  "Even"

paste(xx, ifelse(xx %% 2 == 0, "is Even", "is Odd"))

##  [1] "3 is Odd"   "6 is Even"  "9 is Odd"   "12 is Even" "15 is Odd"  "18 is Even" "21 is Odd"  "24 is Even"
##  [9] "27 is Odd"  "30 is Even"

Objects

Information can be stored in user defined objects, in multiple forms:

c(): a string of values
matrix(): a two dimensional matrix in one format
data.frame(): a two dimensional matrix where each column can be a different format
list():

A string…

xc <- 1:10
xc

##  [1]  1  2  3  4  5  6  7  8  9 10

xc <- c(1,2,3,4,5,6,7,8,9,10)
xc

##  [1]  1  2  3  4  5  6  7  8  9 10

A matrix…

xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = T)
xm

##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]    1    2    3    4    5    6    7    8    9    10
##  [2,]   11   12   13   14   15   16   17   18   19    20
##  [3,]   21   22   23   24   25   26   27   28   29    30
##  [4,]   31   32   33   34   35   36   37   38   39    40
##  [5,]   41   42   43   44   45   46   47   48   49    50
##  [6,]   51   52   53   54   55   56   57   58   59    60
##  [7,]   61   62   63   64   65   66   67   68   69    70
##  [8,]   71   72   73   74   75   76   77   78   79    80
##  [9,]   81   82   83   84   85   86   87   88   89    90
## [10,]   91   92   93   94   95   96   97   98   99   100

xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = F)
xm

##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]    1   11   21   31   41   51   61   71   81    91
##  [2,]    2   12   22   32   42   52   62   72   82    92
##  [3,]    3   13   23   33   43   53   63   73   83    93
##  [4,]    4   14   24   34   44   54   64   74   84    94
##  [5,]    5   15   25   35   45   55   65   75   85    95
##  [6,]    6   16   26   36   46   56   66   76   86    96
##  [7,]    7   17   27   37   47   57   67   77   87    97
##  [8,]    8   18   28   38   48   58   68   78   88    98
##  [9,]    9   19   29   39   49   59   69   79   89    99
## [10,]   10   20   30   40   50   60   70   80   90   100

A data frame…

xd <- data.frame(
  x1 = c("aa","bb","cc","dd","ee",
         "ff","gg","hh","ii","jj"),
  x2 = 1:10,
  x3 = c(1,1,1,1,1,2,2,2,3,3),
  x4 = rep(c(1,2), times = 5),
  x5 = rep(1:5, times = 2),
  x6 = rep(1:5, each = 2),
  x7 = seq(5, 50, by = 5),
  x8 = log10(1:10),
  x9 = (1:10)^3,
  x10 = c(T,T,T,F,F,T,T,F,F,F)
)
xd

##    x1 x2 x3 x4 x5 x6 x7        x8   x9   x10
## 1  aa  1  1  1  1  1  5 0.0000000    1  TRUE
## 2  bb  2  1  2  2  1 10 0.3010300    8  TRUE
## 3  cc  3  1  1  3  2 15 0.4771213   27  TRUE
## 4  dd  4  1  2  4  2 20 0.6020600   64 FALSE
## 5  ee  5  1  1  5  3 25 0.6989700  125 FALSE
## 6  ff  6  2  2  1  3 30 0.7781513  216  TRUE
## 7  gg  7  2  1  2  4 35 0.8450980  343  TRUE
## 8  hh  8  2  2  3  4 40 0.9030900  512 FALSE
## 9  ii  9  3  1  4  5 45 0.9542425  729 FALSE
## 10 jj 10  3  2  5  5 50 1.0000000 1000 FALSE

A list…

xl <- list(xc, xm, xd)
xl[[1]]

##  [1]  1  2  3  4  5  6  7  8  9 10

xl[[2]]

##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]    1   11   21   31   41   51   61   71   81    91
##  [2,]    2   12   22   32   42   52   62   72   82    92
##  [3,]    3   13   23   33   43   53   63   73   83    93
##  [4,]    4   14   24   34   44   54   64   74   84    94
##  [5,]    5   15   25   35   45   55   65   75   85    95
##  [6,]    6   16   26   36   46   56   66   76   86    96
##  [7,]    7   17   27   37   47   57   67   77   87    97
##  [8,]    8   18   28   38   48   58   68   78   88    98
##  [9,]    9   19   29   39   49   59   69   79   89    99
## [10,]   10   20   30   40   50   60   70   80   90   100

xl[[3]]

##    x1 x2 x3 x4 x5 x6 x7        x8   x9   x10
## 1  aa  1  1  1  1  1  5 0.0000000    1  TRUE
## 2  bb  2  1  2  2  1 10 0.3010300    8  TRUE
## 3  cc  3  1  1  3  2 15 0.4771213   27  TRUE
## 4  dd  4  1  2  4  2 20 0.6020600   64 FALSE
## 5  ee  5  1  1  5  3 25 0.6989700  125 FALSE
## 6  ff  6  2  2  1  3 30 0.7781513  216  TRUE
## 7  gg  7  2  1  2  4 35 0.8450980  343  TRUE
## 8  hh  8  2  2  3  4 40 0.9030900  512 FALSE
## 9  ii  9  3  1  4  5 45 0.9542425  729 FALSE
## 10 jj 10  3  2  5  5 50 1.0000000 1000 FALSE

Selecting Data

xc[5] # 5th element in xc

## [1] 5

xd$x3[5] # 5th element in col "x3"

## [1] 1

xd[5,"x3"] # row 5, col "x3"

## [1] 1

xd$x3 # all of col "x3"

##  [1] 1 1 1 1 1 2 2 2 3 3

xd[,"x3"] # all rows, col "x3"

##  [1] 1 1 1 1 1 2 2 2 3 3

xd[3,] # row 3, all cols

##   x1 x2 x3 x4 x5 x6 x7        x8 x9  x10
## 3 cc  3  1  1  3  2 15 0.4771213 27 TRUE

xd[c(2,4),c("x4","x5")] # rows 2 & 4, cols "x4" & "x5"

##   x4 x5
## 2  2  2
## 4  2  4

xl[[3]]$x1 # 3rd object in the list, col "x1

##  [1] "aa" "bb" "cc" "dd" "ee" "ff" "gg" "hh" "ii" "jj"

regexpr

xx <- data.frame(Name = c("Item 1 (detail 1)",
                          "Item 20 (detail 20)",
                          "Item 300 (detail 300)"),
                 Item = NA,
                 Detail = NA)
xx$Detail <- substr(xx$Name, regexpr("\\(", xx$Name)+1, regexpr("\\)", xx$Name)-1)
xx$Item <- substr(xx$Name, 1, regexpr("\\(", xx$Name)-2)
xx

##                    Name     Item     Detail
## 1     Item 1 (detail 1)   Item 1   detail 1
## 2   Item 20 (detail 20)  Item 20  detail 20
## 3 Item 300 (detail 300) Item 300 detail 300

Data Formats

Data can also be saved in many formats:

numeric
integer
character
factor
logical

xd$x3 <- as.character(xd$x3)
xd$x3

##  [1] "1" "1" "1" "1" "1" "2" "2" "2" "3" "3"

xd$x3 <- as.numeric(xd$x3)
xd$x3

##  [1] 1 1 1 1 1 2 2 2 3 3

xd$x3 <- as.factor(xd$x3)
xd$x3

##  [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 1 2 3

xd$x3 <- factor(xd$x3, levels = c("3","2","1"))
xd$x3

##  [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 3 2 1

xd$x10

##  [1]  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE

as.numeric(xd$x10) # TRUE = 1, FALSE = 0

##  [1] 1 1 1 0 0 1 1 0 0 0

sum(xd$x10)

## [1] 5

Internal structure of an object can be checked with str()

str(xc) # c()

##  num [1:10] 1 2 3 4 5 6 7 8 9 10

str(xm) # matrix()

##  int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...

str(xd) # data.frame()

## 'data.frame':    10 obs. of  10 variables:
##  $ x1 : chr  "aa" "bb" "cc" "dd" ...
##  $ x2 : int  1 2 3 4 5 6 7 8 9 10
##  $ x3 : Factor w/ 3 levels "3","2","1": 3 3 3 3 3 2 2 2 1 1
##  $ x4 : num  1 2 1 2 1 2 1 2 1 2
##  $ x5 : int  1 2 3 4 5 1 2 3 4 5
##  $ x6 : int  1 1 2 2 3 3 4 4 5 5
##  $ x7 : num  5 10 15 20 25 30 35 40 45 50
##  $ x8 : num  0 0.301 0.477 0.602 0.699 ...
##  $ x9 : num  1 8 27 64 125 216 343 512 729 1000
##  $ x10: logi  TRUE TRUE TRUE FALSE FALSE TRUE ...

str(xl) # list()

## List of 3
##  $ : num [1:10] 1 2 3 4 5 6 7 8 9 10
##  $ : int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
##  $ :'data.frame':    10 obs. of  10 variables:
##   ..$ x1 : chr [1:10] "aa" "bb" "cc" "dd" ...
##   ..$ x2 : int [1:10] 1 2 3 4 5 6 7 8 9 10
##   ..$ x3 : num [1:10] 1 1 1 1 1 2 2 2 3 3
##   ..$ x4 : num [1:10] 1 2 1 2 1 2 1 2 1 2
##   ..$ x5 : int [1:10] 1 2 3 4 5 1 2 3 4 5
##   ..$ x6 : int [1:10] 1 1 2 2 3 3 4 4 5 5
##   ..$ x7 : num [1:10] 5 10 15 20 25 30 35 40 45 50
##   ..$ x8 : num [1:10] 0 0.301 0.477 0.602 0.699 ...
##   ..$ x9 : num [1:10] 1 8 27 64 125 216 343 512 729 1000
##   ..$ x10: logi [1:10] TRUE TRUE TRUE FALSE FALSE TRUE ...

Packages

Additional libraries can be installed and loaded for use.

install.packages("scales")

library(scales)
xx <- data.frame(Values = 1:10)
xx$Rescaled <- rescale(x = xx$Values, to = c(1,30))
xx

##    Values  Rescaled
## 1       1  1.000000
## 2       2  4.222222
## 3       3  7.444444
## 4       4 10.666667
## 5       5 13.888889
## 6       6 17.111111
## 7       7 20.333333
## 8       8 23.555556
## 9       9 26.777778
## 10     10 30.000000

libraries can also be used without having to load them

scales::rescale(1:10, to = c(1,30))

##  [1]  1.000000  4.222222  7.444444 10.666667 13.888889 17.111111 20.333333 23.555556 26.777778 30.000000

Data Wrangling

R for Data Science - https://r4ds.had.co.nz/

xx <- data.frame(Group = c("X","X","Y","Y","Y","X","X","X","Y","Y"),
                 Data1 = 1:10, 
                 Data2 = seq(10, 100, by = 10))
xx$NewData1 <- xx$Data1 + xx$Data2
xx$NewData2 <- xx$Data1 * 1000
xx

##    Group Data1 Data2 NewData1 NewData2
## 1      X     1    10       11     1000
## 2      X     2    20       22     2000
## 3      Y     3    30       33     3000
## 4      Y     4    40       44     4000
## 5      Y     5    50       55     5000
## 6      X     6    60       66     6000
## 7      X     7    70       77     7000
## 8      X     8    80       88     8000
## 9      Y     9    90       99     9000
## 10     Y    10   100      110    10000

xx$Data1 < 5 # which are less than 5

##  [1]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE

xx[xx$Data1 < 5,]

##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000

xx[xx$Group == "X", c("Group","Data2","NewData1")]

##   Group Data2 NewData1
## 1     X    10       11
## 2     X    20       22
## 6     X    60       66
## 7     X    70       77
## 8     X    80       88

Data wrangling with tidyverse and pipes (%>%)

library(tidyverse) # install.packages("tidyverse")
xx <- data.frame(Group = c("X","X","Y","Y","Y","Y","Y","X","X","X")) %>%
  mutate(Data1 = 1:10, 
         Data2 = seq(10, 100, by = 10),
         NewData1 = Data1 + Data2,
         NewData2 = Data1 * 1000)
xx

##    Group Data1 Data2 NewData1 NewData2
## 1      X     1    10       11     1000
## 2      X     2    20       22     2000
## 3      Y     3    30       33     3000
## 4      Y     4    40       44     4000
## 5      Y     5    50       55     5000
## 6      Y     6    60       66     6000
## 7      Y     7    70       77     7000
## 8      X     8    80       88     8000
## 9      X     9    90       99     9000
## 10     X    10   100      110    10000

filter(xx, Data1 < 5)

##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000

xx %>% filter(Data1 < 5)

##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000

xx %>% filter(Group == "X") %>% 
  select(Group, NewColName=Data2, NewData1)

##   Group NewColName NewData1
## 1     X         10       11
## 2     X         20       22
## 3     X         80       88
## 4     X         90       99
## 5     X        100      110

xs <- xx %>% 
  group_by(Group) %>% 
  summarise(Data2_mean = mean(Data2),
            Data2_sd = sd(Data2),
            NewData2_mean = mean(NewData2),
            NewData2_sd = sd(NewData2))
xs

## # A tibble: 2 × 5
##   Group Data2_mean Data2_sd NewData2_mean NewData2_sd
##   <chr>      <dbl>    <dbl>         <dbl>       <dbl>
## 1 X             60     41.8          6000       4183.
## 2 Y             50     15.8          5000       1581.

xx %>% left_join(xs, by = "Group")

##    Group Data1 Data2 NewData1 NewData2 Data2_mean Data2_sd NewData2_mean NewData2_sd
## 1      X     1    10       11     1000         60 41.83300          6000    4183.300
## 2      X     2    20       22     2000         60 41.83300          6000    4183.300
## 3      Y     3    30       33     3000         50 15.81139          5000    1581.139
## 4      Y     4    40       44     4000         50 15.81139          5000    1581.139
## 5      Y     5    50       55     5000         50 15.81139          5000    1581.139
## 6      Y     6    60       66     6000         50 15.81139          5000    1581.139
## 7      Y     7    70       77     7000         50 15.81139          5000    1581.139
## 8      X     8    80       88     8000         60 41.83300          6000    4183.300
## 9      X     9    90       99     9000         60 41.83300          6000    4183.300
## 10     X    10   100      110    10000         60 41.83300          6000    4183.300

Read/Write data

xx <- read.csv("data_r_tutorial.csv")
write.csv(xx, "data_r_tutorial.csv", row.names = F)

For excel sheets, the package readxl can be used to read in sheets of data.

library(readxl) # install.packages("readxl")
xx <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data")

Tidy Data

Tutorial 1 - https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html

Tutorial 2 - https://r4ds.had.co.nz/tidy-data.html

yy <- xx %>%
  group_by(Name, Location) %>%
  summarise(Mean_DTF = round(mean(DTF),1)) %>% 
  arrange(Location)
yy

## # A tibble: 9 × 3
## # Groups:   Name [3]
##   Name          Location            Mean_DTF
##   <chr>         <chr>                  <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh     86.7
## 2 ILL 618 AGL   Jessore, Bangladesh     79.3
## 3 Laird AGL     Jessore, Bangladesh     76.8
## 4 CDC Maxim AGL Metaponto, Italy       134. 
## 5 ILL 618 AGL   Metaponto, Italy       138. 
## 6 Laird AGL     Metaponto, Italy       137. 
## 7 CDC Maxim AGL Saskatoon, Canada       52.5
## 8 ILL 618 AGL   Saskatoon, Canada       47  
## 9 Laird AGL     Saskatoon, Canada       56.8

yy <- yy %>% spread(key = Location, value = Mean_DTF)
yy

## # A tibble: 3 × 4
## # Groups:   Name [3]
##   Name          `Jessore, Bangladesh` `Metaponto, Italy` `Saskatoon, Canada`
##   <chr>                         <dbl>              <dbl>               <dbl>
## 1 CDC Maxim AGL                  86.7               134.                52.5
## 2 ILL 618 AGL                    79.3               138.                47  
## 3 Laird AGL                      76.8               137.                56.8

yy <- yy %>% gather(key = TraitName, value = Value, 2:4)
yy

## # A tibble: 9 × 3
## # Groups:   Name [3]
##   Name          TraitName           Value
##   <chr>         <chr>               <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh  86.7
## 2 ILL 618 AGL   Jessore, Bangladesh  79.3
## 3 Laird AGL     Jessore, Bangladesh  76.8
## 4 CDC Maxim AGL Metaponto, Italy    134. 
## 5 ILL 618 AGL   Metaponto, Italy    138. 
## 6 Laird AGL     Metaponto, Italy    137. 
## 7 CDC Maxim AGL Saskatoon, Canada    52.5
## 8 ILL 618 AGL   Saskatoon, Canada    47  
## 9 Laird AGL     Saskatoon, Canada    56.8

yy <- yy %>% spread(key = Name, value = Value)
yy

## # A tibble: 3 × 4
##   TraitName           `CDC Maxim AGL` `ILL 618 AGL` `Laird AGL`
##   <chr>                         <dbl>         <dbl>       <dbl>
## 1 Jessore, Bangladesh            86.7          79.3        76.8
## 2 Metaponto, Italy              134.          138.        137. 
## 3 Saskatoon, Canada              52.5          47          56.8

Base Plotting

We will start with some basic plotting using the base function plot()

Tutorial 1 - http://www.sthda.com/english/wiki/r-base-graphs

Tutorial 2 - https://bookdown.org/rdpeng/exdata/the-base-plotting-system-1.html

# A basic scatter plot
plot(x = xd$x8, y = xd$x9)

# Adjust color and shape of the points
plot(x = xd$x8, y = xd$x9, col = "darkred", pch = 0)

plot(x = xd$x8, y = xd$x9, col = xd$x4, pch = xd$x4)

# Adjust plot type 
plot(x = xd$x8, y = xd$x9, type = "line")

# Adjust linetype
plot(x = xd$x8, y = xd$x9, type = "line", lty = 2)

# Plot lines and points
plot(x = xd$x8, y = xd$x9, type = "both")

Now lets create some random and normally distributed data to make some more complicated plots

# 100 random uniformly distributed numbers ranging from 0 - 100
ru <- runif(100, min = 0, max = 100)
ru

##   [1] 49.016442 75.979688 96.584707 86.433259 60.413476 68.396913 24.314633 18.759369 45.995786 75.206145
##  [11]  9.295966  4.231271  4.286971 65.049048 12.470255 83.366414 85.708599 78.808489 52.876977 36.634654
##  [21] 71.524863  9.803109 42.162989 37.386858 25.021478 19.127309 96.154311 87.302090 17.293362 71.048324
##  [31] 66.878810 17.097226 59.950611  6.838500  1.242704 78.431516 98.047588 31.120112 25.171182 88.984168
##  [41] 63.730688 31.400130 52.096809 75.361409 88.120357 76.826751 39.103663 79.880326  7.797166 37.452887
##  [51] 39.806544 19.471004 19.150540 27.886626 17.385538 47.585887 95.218242 78.872699 11.582288 63.681898
##  [61] 63.792469 12.332816 87.090244 25.234537  1.712325 18.242388 51.952563 62.400025 73.669996 37.003981
##  [71] 95.446658 90.942862 92.493387 18.637321 90.294896 51.176555 26.107782 78.344975 50.956096 59.538699
##  [81] 56.917712 64.227556  5.180972 57.398075  4.400496 35.204842 83.366260 83.525348 50.536930 24.869214
##  [91] 14.977451  6.525174 86.998399 52.723321 44.779710 67.548543 91.713303 73.662490 35.366837 48.464965

plot(x = ru)

order(ru)

##   [1]  35  65  12  13  85  83  92  34  49  11  22  59  62  15  91  32  29  55  66  74   8  26  53  52   7  90
##  [27]  25  39  64  77  54  38  42  86  99  20  70  24  50  47  51  23  95   9  56 100   1  89  79  76  67  43
##  [53]  94  19  81  84  80  33   5  68  60  41  61  82  14  31  96   6  30  21  98  69  10  44   2  46  78  36
##  [79]  18  58  48  87  16  88  17   4  93  63  28  45  40  75  72  97  73  57  71  27   3  37

ru<- ru[order(ru)]
ru

##   [1]  1.242704  1.712325  4.231271  4.286971  4.400496  5.180972  6.525174  6.838500  7.797166  9.295966
##  [11]  9.803109 11.582288 12.332816 12.470255 14.977451 17.097226 17.293362 17.385538 18.242388 18.637321
##  [21] 18.759369 19.127309 19.150540 19.471004 24.314633 24.869214 25.021478 25.171182 25.234537 26.107782
##  [31] 27.886626 31.120112 31.400130 35.204842 35.366837 36.634654 37.003981 37.386858 37.452887 39.103663
##  [41] 39.806544 42.162989 44.779710 45.995786 47.585887 48.464965 49.016442 50.536930 50.956096 51.176555
##  [51] 51.952563 52.096809 52.723321 52.876977 56.917712 57.398075 59.538699 59.950611 60.413476 62.400025
##  [61] 63.681898 63.730688 63.792469 64.227556 65.049048 66.878810 67.548543 68.396913 71.048324 71.524863
##  [71] 73.662490 73.669996 75.206145 75.361409 75.979688 76.826751 78.344975 78.431516 78.808489 78.872699
##  [81] 79.880326 83.366260 83.366414 83.525348 85.708599 86.433259 86.998399 87.090244 87.302090 88.120357
##  [91] 88.984168 90.294896 90.942862 91.713303 92.493387 95.218242 95.446658 96.154311 96.584707 98.047588

plot(x = ru)

# 100 normally distributed numbers with a mean of 50 and sd of 10
nd <- rnorm(100, mean = 50, sd = 10)
nd

##   [1] 46.49653 45.91375 33.25639 52.76651 57.91105 43.34187 48.14945 53.23660 55.27801 56.42600 33.89935
##  [12] 51.58160 57.97050 52.43681 48.52891 54.72067 62.27335 62.88484 44.75399 55.66505 45.62428 49.06864
##  [23] 47.89180 62.68028 58.95349 52.68051 34.61866 52.81122 53.34170 59.53093 57.11837 47.60959 43.72208
##  [34] 63.51286 57.86001 52.79110 47.01787 53.24222 48.09364 52.99298 71.42377 45.61210 48.31099 61.44173
##  [45] 58.81346 54.55549 43.17735 46.30588 52.83955 29.82481 39.99413 52.81789 55.50990 49.68982 48.64055
##  [56] 50.91239 55.51283 49.84187 59.55021 36.76456 52.44588 42.24262 46.15806 55.16557 48.50282 53.12795
##  [67] 41.97917 57.33084 55.98157 46.86929 50.05560 53.20316 48.03697 55.79204 26.34130 38.18252 47.96142
##  [78] 46.32000 35.09744 59.94100 46.37788 66.72394 37.01928 57.44401 51.03991 40.60931 34.14069 46.80812
##  [89] 65.93207 37.75612 46.02138 36.69877 43.03057 56.07953 54.15652 34.41328 44.21481 54.79065 53.61810
## [100] 46.36454

nd <- nd[order(nd)]
nd

##   [1] 26.34130 29.82481 33.25639 33.89935 34.14069 34.41328 34.61866 35.09744 36.69877 36.76456 37.01928
##  [12] 37.75612 38.18252 39.99413 40.60931 41.97917 42.24262 43.03057 43.17735 43.34187 43.72208 44.21481
##  [23] 44.75399 45.61210 45.62428 45.91375 46.02138 46.15806 46.30588 46.32000 46.36454 46.37788 46.49653
##  [34] 46.80812 46.86929 47.01787 47.60959 47.89180 47.96142 48.03697 48.09364 48.14945 48.31099 48.50282
##  [45] 48.52891 48.64055 49.06864 49.68982 49.84187 50.05560 50.91239 51.03991 51.58160 52.43681 52.44588
##  [56] 52.68051 52.76651 52.79110 52.81122 52.81789 52.83955 52.99298 53.12795 53.20316 53.23660 53.24222
##  [67] 53.34170 53.61810 54.15652 54.55549 54.72067 54.79065 55.16557 55.27801 55.50990 55.51283 55.66505
##  [78] 55.79204 55.98157 56.07953 56.42600 57.11837 57.33084 57.44401 57.86001 57.91105 57.97050 58.81346
##  [89] 58.95349 59.53093 59.55021 59.94100 61.44173 62.27335 62.68028 62.88484 63.51286 65.93207 66.72394
## [100] 71.42377

plot(x = nd)

hist(x = nd)

hist(nd, breaks = 20, col = "darkgreen")

plot(x = density(nd))

boxplot(x = nd)

boxplot(x = nd, horizontal = T)

ggplot2

Lets be honest, the base plots are ugly! The ggplot2 package gives the user to create a better, more visually appealing plots. Additional packages such as ggbeeswarm and ggrepel also contain useful functions to add to the functionality of ggplot2.

ggplot2 - https://ggplot2.tidyverse.org/

Tutorial 1 - http://r-statistics.co/ggplot2-Tutorial-With-R.html

Tutorial 2 - https://www.statsandr.com/blog/graphics-in-r-with-ggplot2/

The R Graph Gallery - https://www.r-graph-gallery.com/ggplot2-package.html

library(ggplot2)
mp <- ggplot(xd, aes(x = x8, y = x9))
mp + geom_point()

mp + geom_point(aes(color = x3, shape = x3), size = 4)

mp + geom_line(size = 2)

mp + geom_line(aes(color = x3), size = 2)

mp + geom_smooth(method = "loess")

mp + geom_smooth(method = "lm")

xx <- data.frame(data = c(rnorm(50, mean = 40, sd = 10),
                          rnorm(50, mean = 60, sd = 5)),
                 group = factor(rep(1:2, each = 50)),
                 label = c("Label1", rep(NA, 49), "Label2", rep(NA, 49)))
mp <- ggplot(xx, aes(x = data, fill = group))
mp + geom_histogram(color = "black")

mp + geom_histogram(color = "black", position = "dodge")

mp1 <- mp + geom_histogram(color = "black") + facet_grid(group~.)
mp1

mp + geom_density(alpha = 0.5)

mp <- ggplot(xx, aes(x = group, y = data, fill = group))
mp + geom_boxplot(color = "black")

mp + geom_boxplot() + geom_point()

mp + geom_violin() + geom_boxplot(width = 0.1, fill = "white")

library(ggbeeswarm)
mp + geom_quasirandom()

mp + geom_quasirandom(aes(shape = group))

mp2 <- mp + geom_violin() + 
  geom_boxplot(width = 0.1, fill = "white") +
  geom_beeswarm(alpha = 0.5)
library(ggrepel)
mp2 + geom_text_repel(aes(label = label), nudge_x = 0.4)

library(ggpubr)
ggarrange(mp1, mp2, ncol = 2, widths = c(2,1),
          common.legend = T, legend = "bottom")

Statistics

Handbook of Biological Statistics - http://biostathandbook.com/

R Companion for ^ - https://rcompanion.org/rcompanion/a_02.html

# Prep data
lev_Loc  <- c("Saskatoon, Canada", "Jessore, Bangladesh", "Metaponto, Italy")
lev_Name <- c("ILL 618 AGL", "CDC Maxim AGL", "Laird AGL")
dd <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data") %>%
  mutate(Location = factor(Location, levels = lev_Loc),
         Name = factor(Name, levels = lev_Name))
xx <- dd %>%
  group_by(Name, Location) %>%
  summarise(Mean_DTF = mean(DTF))
xx %>% spread(Location, Mean_DTF)

## # A tibble: 3 × 4
## # Groups:   Name [3]
##   Name          `Saskatoon, Canada` `Jessore, Bangladesh` `Metaponto, Italy`
##   <fct>                       <dbl>                 <dbl>              <dbl>
## 1 ILL 618 AGL                  47                    79.3               138.
## 2 CDC Maxim AGL                52.5                  86.7               134.
## 3 Laird AGL                    56.8                  76.8               137.

# Plot
mp1 <- ggplot(dd, aes(x = Location, y = DTF, color = Name, shape = Name)) +
  geom_point(size = 2, alpha = 0.7, position = position_dodge(width=0.5))
mp2 <- ggplot(xx, aes(x = Location, y = Mean_DTF, 
                      color = Name, group = Name, shape = Name)) +
  geom_point(size = 2.5, alpha = 0.7) + 
  geom_line(size = 1, alpha = 0.7) +
  theme(legend.position = "top")
ggarrange(mp1, mp2, ncol = 2, common.legend = T, legend = "top")

From first glace, it is clear there are differences between genotypes, locations, and genotype x environment (GxE) interactions. Now let’s do a few statistical tests.

summary(aov(DTF ~ Name * Location, data = dd))

##               Df Sum Sq Mean Sq  F value   Pr(>F)    
## Name           2     88      44    3.476   0.0395 *  
## Location       2  65863   32931 2598.336  < 2e-16 ***
## Name:Location  4    560     140   11.044 2.52e-06 ***
## Residuals     45    570      13                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

As expected, an ANOVA shows statistical significance for genotype (p-value = 0.0395), Location (p-value < 2e-16) and GxE interactions (p-value < 2.52e-06). However, all this tells us is that one genotype is different from the rest, one location is different from the others and that there is GxE interactions. If we want to be more specific, would need to do some multiple comparison tests.

If we only have two things to compare, we could do a t-test.

xx <- dd %>% 
  filter(Location %in% c("Saskatoon, Canada", "Jessore, Bangladesh")) %>%
  spread(Location, DTF)
t.test(x = xx$`Saskatoon, Canada`, y = xx$`Jessore, Bangladesh`)

## 
##  Welch Two Sample t-test
## 
## data:  xx$`Saskatoon, Canada` and xx$`Jessore, Bangladesh`
## t = -17.521, df = 32.701, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -32.18265 -25.48402
## sample estimates:
## mean of x mean of y 
##  52.11111  80.94444

DTF in Saskatoon, Canada is significantly different (p-value < 2.2e-16) from DTF in Jessore, Bangladesh.

xx <- dd %>% 
  filter(Name %in% c("ILL 618 AGL", "Laird AGL"),
         Location == "Metaponto, Italy") %>%
  spread(Name, DTF)
t.test(x = xx$`ILL 618 AGL`, y = xx$`Laird AGL`)

## 
##  Welch Two Sample t-test
## 
## data:  xx$`ILL 618 AGL` and xx$`Laird AGL`
## t = 0.38008, df = 8.0564, p-value = 0.7137
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -5.059739  7.059739
## sample estimates:
## mean of x mean of y 
##  137.8333  136.8333

DTF between ILL 618 AGL and Laird AGL are not significantly different (p-value = 0.7137) in Metaponto, Italy.

pch Plot

xx <- data.frame(x = rep(1:6, times = 5, length.out = 26),
                 y = rep(5:1, each = 6, length.out = 26),
                 pch = 0:25)
mp <- ggplot(xx, aes(x = x, y = y, shape = as.factor(pch))) +
  geom_point(color = "darkred", fill = "darkblue", size = 5) +
  geom_text(aes(label = pch), nudge_x = -0.25) +
  scale_shape_manual(values = xx$pch) +
  scale_x_continuous(breaks = 6:1) +
  scale_y_continuous(breaks = 6:1) +
  theme_void() +
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5),
        axis.text = element_blank(),
        axis.ticks = element_blank()) +
  labs(title = "Plot symbols in R (pch)",
       subtitle = "color = \"darkred\", fill = \"darkblue\"",
       x = NULL, y = NULL)
ggsave("pch.png", mp, width = 4.5, height = 3, bg = "white")

R Markdown

Tutorials on how to create an R markdown document like this one can be found here:

https://rmarkdown.rstudio.com/articles_intro.html

https://rmarkdown.rstudio.com/lesson-1.html

https://alexd106.github.io/intro2R/Rmarkdown_intro.html

R Tutorial

An introduction to R

R Tutorial

Introduction

Download

Other Useful Tutorials

Calculator

Functions

Custom Functions

for loops and if else statements

Objects

A string…

A matrix…

A data frame…

A list…

Selecting Data

regexpr

Data Formats

Packages

Data Wrangling

Read/Write data

Tidy Data

Base Plotting

ggplot2

Statistics

pch Plot

R Markdown

`for` loops and `if` `else` statements